Face Recognition by Matching 2D and 3D Geodesic Distances

Face recognition has been addressed both in 2D, using still images or video sequences, and in 3D using three-dimensional face models. In this paper, we propose an original framework which provides a description capable to support 3D-3D face recognition as well as to directly compare 2D face images against 3D face models. This representation is extracted by measuring geodesic distances in 3D and 2D. In 3D, the geodesic distance between two points on a surface is computed as the length of the shortest path connecting the two points on the model. In 2D, the geodesic distance between two pixels is computed based on the differences of gray level intensities along the segment connecting the two pixels in the image. Experimental results are reported for 3D-3D and 2D-3D face recognition, in order to demonstrate the viability of the proposed approach.